The impacts of various parameters on learning process and machine learning based performance prediction in online coding competitions

TL;DR

This study investigates how student behavior, emotions, and problem difficulty influence performance in online coding competitions, using surveys, classification models, and analysis of submission data.

Contribution

It introduces a detailed analysis of parameters affecting learning and performance prediction in online coding contests, including new difficulty level classifications and machine learning evaluation.

Findings

Difficulty levels significantly affect performance prediction accuracy.

Emotions and mood impact submission times and success rates.

Machine learning classifiers effectively predict submission outcomes based on parameters.

Abstract

Various parameters affect the performance of students in online coding competitions. Students' behavior, approach, emotions, and problem difficulty levels significantly impact their performance in online coding competitions. We have organized two coding competitions to understand the effects of the above parameters. We have done the online survey at the end of each coding competition, and it contains questions related to the behavior, approach, and emotions of students during online coding competitions. Students are evaluated based on the time and status of the submissions. We have carried out a detailed analysis to address the impact of students' approach, behavior, and emotions on the learning process in online coding competitions. Two difficulty levels are proposed based on the time and status of submissions. The impact of difficulty levels on machine learning-based performance prediction is presented in this research work. Based on time, the coding solution submissions have two classes "Less than 15 minutes" and "More than 15 minutes". There are three classes, "Complete solution", "Partial solution", and "Not submitted at all," based on the submission status. The appropriate approaches are found for both the coding competitions to submit the solution within 15 minutes. Machine learning classifiers are trained and evaluated for the above classification problems. The impacts of mood, emotions, and difficulty levels on the learning process are also assessed by comparing the results of machine learning models for both coding competitions.